TokenLight: Precise Lighting Control in Images using Attribute Tokens

TL;DR

TokenLight introduces a novel image relighting method using attribute tokens for precise control over multiple lighting factors, trained on synthetic data and validated on real images, achieving state-of-the-art results.

Contribution

The paper proposes attribute tokens for encoding lighting attributes, enabling continuous relighting control without explicit inverse rendering supervision.

Findings

Achieves state-of-the-art quantitative and qualitative relighting performance.

Demonstrates understanding of scene geometry and materials without explicit supervision.

Handles challenging scenarios like interior object lighting and transparent materials convincingly.

Abstract

This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulate relighting as a conditional image generation task and introduce attribute tokens to encode distinct lighting factors such as intensity, color, ambient illumination, diffuse level, and 3D light positions. The model is trained on a large-scale synthetic dataset with ground-truth lighting annotations, supplemented by a small set of real captures to enhance realism and generalization. We validate our approach across a variety of relighting tasks, including controlling in-scene lighting fixtures and editing environment illumination using virtual light sources, on synthetic and real images. Our method achieves state-of-the-art quantitative and qualitative performance compared to prior work. Remarkably, without explicit inverse rendering supervision, the model exhibits an inherent understanding of how light interacts with scene geometry, occlusion, and materials, yielding convincing lighting effects even in traditionally challenging scenarios such as placing lights within objects or relighting transparent materials plausibly. Project page: vrroom.github.io/tokenlight/